Summary of Best Evidence for Prevention of Recurrence of Late-life Depression
Bibliographic record
Abstract
Background The recurrence rate of late-life depression is high, however, there is a lack of research on intervention protocol to prevent recurrence of late-life depression at home and abroad.In the early stage, the research group explored the risk factors for recurrence of late-life depression, and the controllable risk factors were poor medication compliance, less physical activity, and less eating. Objective To search, evaluate and summarize the evidence related to the prevention of recurrence of late-life depression based on the above three risk factors. Methods In October 2022, the evidences related to the prevention of recurrence of late-life depression were searched in Web of Science, PubMed, Cochrane Library, American Guide Network, British Guidebook, Website of Registered Nurses Association of Ontario (RNAO), Evidence-based Nursing Center of Fudan University, CNKI and other databases from inception to October 2022, which include guidelines, systematic reviews, expert consensuses, evidence summaries, etc. The quality of the included guidelines, systematic reviews, expert consensuses and original researches were evaluated by investigators trained in systematic evidence-based medicine using the Appraisal of Guidelines for Research & EvaluationⅡ (AGREEⅡ) Instrument, AMSTAR, and the Australian Joanna Briggs Institute (JBI) evidence-based center evaluation standards (2016 version), respectively. Finally, the 2014 Australian JBI evidence-based health care center evidence grading and recommendation level system was used to summary, evaluate and grade the evidence. Results A total of 15 articles were included, including 3 guidelines, 11 systematic reviews and 1 RCT. The qualities of the 3 guidelines were graded as B or above, the qualities of 11 systematic reviews were graded as medium or above, and the quality of 1 RCT was graded as B. A total of 24 best evidence items were summarized from 6 aspects of intervention evaluation, diets and nutrition, exercise and rehabilitation training, psychosocial interventions, medication compliance, and effect evaluation. Conclusion The study summarizes the best evidence for prevention of late-life depression, suggesting that clinical staff should comprehensively evaluate the patients, and develop individualized intervention program focus on evaluation, diets and nutrition, exercise and rehabilitation training, medication compliance guidance, and psychosocial interventions, combining with the characteristics of patients and diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".